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arXiv 2601.15917cond-mat.mtrl-sci
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通过化学气相沉积生长的双层MoS2基人工光电子突触用于算术计算和图像识别应用

CVD grown bilayer MoS2 based artificial optoelectronic synapses for arithmetic computing and image recognition applications

Indian Institute of Technology, Bombay(孟买印度理工学院);National Institute for Materials Science(日本国立材料研究所);Jackson State University(杰克逊州立大学);Institute of Physics, Bhubaneswar(布巴内斯瓦尔物理研究所);Homi Bhabha National Institute(霍米·巴巴国家研究所)

Umakanta Patra, Subhrajit Sikdar, Roshan Padhan, Amandeep Kaur, Satyaprakash Sahoo, Subhabrata Dhar

AI总结:

基于双层MoS2的突触器件实现低功耗神经形态计算,具备算术运算和图像识别能力。

摘要:

对计算能力的需求迅速增加。在此背景下,整合数据存储和处理的类脑神经形态计算引起了广泛关注。我们的研究发现,在化学气相沉积双层(2L)MoS2薄膜上制造的场效应晶体管可以模拟生物突触的功能。这些设备表现出高水平的脉冲对促进(PPF)、短期记忆到长期记忆(STM-to-LTM)的转变以及学习-遗忘-再学习特性。研究了光强度、脉冲数、脉冲宽度和光子能量对STM-to-LTM转变的影响。发现可以通过栅极偏置控制记忆状态的衰减率。每突触事件的电能和光能消耗分别估计为280 fJ和20 nJ。此外,这些设备的光电流随着激发脉冲数的增加而线性增加。这一特性已被用于通过设备演示不同的算术运算。此外,这些设备在图像识别方面显示出巨大潜力。人工神经网络模拟返回了约85%的图像识别准确率。所有这些发现表明,2L-MoS2在开发低功耗、透明和灵活的神经形态设备方面具有广阔前景。

英文摘要:

Demand for lower computing power has rapidly increased. In this context, brain-inspired neuromorphic computing, which integrate data storage and processing, has attracted significant attention. Here, our study reveals that field effect transistors fabricated on chemical vapor deposited bilayer (2L) MoS2 films can mimic the functions of biological synapse. These devices demonstrate high level of pair pulse facilitation (PPF), short term to long term memory (STM-to-LTM) transition as well as learning-forgetting-relearning properties. Effect of light intensity, pulse number, pulse width and photon energy on the STM-to-LTM transition is studied. It has been found that the rate of depression of the memory state can be controlled using the gate bias. Electrical and optical energy consumptions per synaptic event are estimated to be as low as 280 fJ and 20 nJ, respectively. Furthermore, photocurrent in these devices is observed to increase linearly with the number of the excitation pulses. This property has been exploited to demonstrate different arithmetic operations by the device. Moreover, these devices show great potential for image recognition. Artificial neural network simulation has returned an image recognition accuracy of ~85%. All these findings show a great prospect of 2L-MoS2 for developing low power, transparent and flexible neuromorphic devices.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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